<p>This study develops a novel tri-methodological computational framework to examine gender-differentiated adoption of information and communication technology (ICT) and green production technologies in Pakistan’s agricultural sector. Unlike prior studies that rely on a single analytical approach, this research uniquely integrates macro-level time-series econometrics (Autoregressive distributed lag—ARDL), micro-level behavioral modeling (Structural equation modeling—SEM), and advanced machine learning (ML) algorithms to bridge the macro–micro divide in technology adoption research. Using national time-series data (2002–2022), the ARDL results confirm a robust long-run positive relationship between ICT expansion and agricultural productivity. However, micro-level SEM analysis reveals a pronounced gender paradox: while women farmers demonstrate strong pro-environmental values and innovative intentions, their actual adoption of green technologies is significantly constrained by structural barriers such as limited access to land, credit, and digital assets. Complementary ML models identify water availability, credit access, and mechanization as the most powerful nonlinear predictors of agricultural performance—precisely the resources from which women are disproportionately excluded. The study further develops an optimization-based policy framework, showing that reducing gender-based digital and institutional barriers could raise agricultural output by 20–30% and increase Pakistan’s GDP by up to 2%. These findings provide a data-driven roadmap for designing gender-responsive digital and green agricultural policies in developing economies.</p>